Biology subjects

Wood, K. B.

Publications and source records attributed to Wood, K. B..

At least 19 recordsLinked to original sources

bioRxiv2026

Cooperative antibiotic response in coupled biofilm and planktonic E. faecalis communities

Bacterial communities grow as dynamic populations that respond to their environments. A clinically relevant example is the inactivation of beta-lactam antibiotics by intracellular beta-lactamase in E. faecalis resistant strains. In these populations, resistant bacteria act as antibiotic sinks, detoxifying the environment and allowing sensitive bacteria to survive treatment through a cooperative interaction. In this work, we study strongly coupled planktonic and biofilm populations of mixed sensitive-resistant E. faecalis bacteria under antibiotic stress using fluorescent microscopy. The presence of resistant bacteria in the system benefits both resistant and sensitive cells, leading to mixed planktonic and biofilm populations at super-inhibitory drug concentrations. We show that a beta-lactam antibiotic with or without the addition of a beta-lactam inhibitor can lead to a population inversion effect, characterized by a non-monotonic relation between initial and final fractions of resistant bacteria. The effect is observed in both the planktonic and biofilm populations and is modulated by the total initial cell density. A well-mixed model with competition mediated by resource sharing and cooperation from global degradation of toxins predicts the experimentally observed behavior. These observations suggest underlying population-level mechanisms that are largely independent of biofilm spatial structure.

biophysics↗

bioRxiv2026

Minding the gap: collective determinants of multiscale structure across interacting bacterial colonies

A bacterial colony rarely exists in isolation - in natural habitats, colonies interact to form spatially structured communities across length and time scales. Eco-evolutionary feedbacks link these scales, such that structure at one level can influence another, yet the interplay between single- and multi-colony organization remains poorly understood. As a step toward addressing this, we develop a high-throughput platform to track population dynamics across spatially extended networks of colonies. A common structural feature observed at the multi-colony scale is the formation of a stable gap region between colonies, even when they are isogenic. Numerous studies observe similar patterns of behavior across species, with few resolving the underlying mechanism. Here, we ask: what are the minimal ingredients shaping this multi-colony structure? We focus on colonies of the opportunistic pathogen Enterococcus faecalis, a model organism for which this behavior has yet to be reported. By combining modeling and experiments, we show that both nutrient competition and direct growth inhibition control colony morphology and expansion of interacting colonies. We identify distinct regimes of gap formation, relating intra- and inter-colony spatial patterns to ecological interactions mediated at the cellular scale. Together, our results suggest that antagonism, even between isogenic populations through self-inhibition, is likely a common behavior of bacterial species in general.

ecology↗

bioRxiv2025

Deciphering population-level response under spatial drug heterogeneity on microhabitat structures

Bacteria and cancer cells inhabit spatially heterogeneous environments, where migration shapes microhabitat structures critical for colonization and metastasis. The interplay between growth, migration, and spatial structure complicates the prediction of population responses to drug treatment--such as clearance or persistence--even under the same spatially averaged growth rate. Accurately predicting these responses is essential for designing effective treatment strategies. Here, we propose a minimal growth-migration model to study population dynamics on discrete microhabitat structures under spatial drug heterogeneity. By applying a kernel transformation, we map the original structure to an effective fully connected graph and derive a new exact criterion for population response based on a regularized Laplacian kernel reweighted by local growth rates. This criterion connects to forest closeness centrality and yields analytical bounds and sufficient conditions for population growth or decline. We find that higher structural connectivity--like increased migration--generally promotes decline. Our framework also informs optimal spatial drug assignments, which reduce to selecting interconnected subcores in the effective complete graph. For partially controllable microhabitats or unknown drug distributions, we identify strategies that ensure population decline. Overall, our results offer a new theoretical perspective on drug response in spatially structured populations and provide practical guidance for optimizing spatially explicit dosing strategies in heterogeneous environments.

biophysics↗

bioRxiv2024

Spatial drug asymmetry modulates phenotypic diversity-migration relationships under resistance evolution

At long timescales, resistant phenotypes will emerge and be selected within the bacterial population as an evolutionary response to drug exposure. This phenomenon reduces the efficacy of drug therapies and thus compromises patient health. In spatially heterogeneous drug environments, recent evidence shows that migration can either promote or decelerate the evolution of antibiotic resistance, thereby affecting the rate of resistant phenotype emergence. However, another important quantitative aspect of resistance evolution--bacterial phenotypic diversity--has often been overlooked and remains challenging to investigate in spatially extended systems, both experimentally and clinically. In order to study how diversity is reshaped by migration across space, here we designed a minimal 2-well experimental system with spatial drug asymmetry. One well contained a bacteriostatic drug (Linezolid) at the minimum inhibitory concentration, while the other well served as a sanctuary with just media. We found that the relationship between diversity and migration follows the "Intermediate Disturbance Hypothesis" (IDH), with migration as the disturbance to each well. By varying the selective drug concentrations, we observed that the diversity-migration relationship changes, and IDH can disappear. This behavior was explained by an asymmetry parameter derived from a two-phenotype growth-migration dynamic model. To further validate how different spatial drug asymmetries modulate the diversity-migration relationship through this asymmetry parameter, we applied another bactericidal drug, Ampicillin, and observed similar results. In a more complex scenario involving both Linezolid and Ampicillin, four distinct phenotypes, including cross-resistant variants, emerged. Our asymmetry parameter successfully explained the diversity-migration relationship, with unique diversity dynamics such as multiple peaks appearing from the model. The minimal generalist-specialist framework predicted these unique behaviors through global fitness advantages. Our findings provide experimental support and theoretical explanations for the emergence of phenotypic diversity in clinical settings, such as gut-lung translocation. These insights may pave the way for improved clinical strategies to manage antibiotic resistance evolution.

evolutionary biology↗

bioRxiv2024

Spatial pattern formation of bacterial colonies with social antibiotic resistance

Bacteria frequently inhabit surface-attached communities where rich "social" interactions can significantly alter their population-level behavior, including their response to antibiotics. Understanding these collective effects in spatially heterogeneous communities is an ongoing challenge. Here, we investigated the spatial organization that emerges from antibiotic exposure in initially randomly distributed communities containing antibiotic-resistant and -sensitive strains of E. faecalis, an opportunistic pathogen. We identified that a range of complex spatial structures emerged in the population homeland--the inoculated region that microbes inhabit prior to range expansion--, which depended on initial colony composition and antibiotic concentration. We found that these arrangements were explained by cooperative interactions between resistant and sensitive subpopulations with a variable spatial scale, the result of dynamic zones of protection afforded to sensitive cells by growing populations of enzyme-producing resistant neighbors. Using a combination of experiments and mathematical models, we explored the complex spatiotemporal interaction dynamics that create these patterns, and predicted spatial arrangements of sensitive and resistant subpopulations under new conditions. We illustrated how spatial population dynamics in the homeland affect subsequent range expansion, both because they modulate the composition of the initial expanding front, and through long-range cooperation between the homeland and the expanding region. Finally, we showed that these spatial constraints resulted in populations whose size and composition differed markedly from matched populations in well-stirred (planktonic) cultures. These findings underscore the importance of spatial structure and cooperation, long-studied features in theoretical ecology, for determining the fate of bacterial communities under antibiotic exposure. SignificanceInteractions between bacteria are common, particularly in the crowded surface-associated communities that occur anywhere from natural ecosystems to the human body to medical devices. Antibiotic resistance can be influenced by these "social" interactions, making it difficult to predict how spatial communities respond to antibiotic. Here, we show that complex spatial arrangements emerge when initially randomly distributed populations of antibiotic-resistant and -sensitive E. faecalis, a microbial pathogen, are exposed to antibiotic. Using mathematical models and experiments, we show how local competition and dynamic-range cross-protection drive pattern formation. As a result, these spatially structured populations respond differently to antibiotics than well-mixed communities. Our findings elucidate how "social" antibiotic resistance affects spatially structured bacterial communities, a step towards predicting and controlling resistance.

biophysics↗

bioRxiv2023

Heterogeneous collateral effects in daptomycin-resistant E. faecalis

Daptomycin, a cyclic lipopeptide antibiotic that targets the cell membrane, is an important therapeutic option for treating multi-drug-resistant infections, including vancomycin-resistant enterococci (VRE). Recent work has uncovered an array of daptomycin resistance mechanisms in enterococci, but relatively little is known about how these molecular defenses contribute to collateral effects-that is, to increased resistance or sensitivity to other drugs. In this work, we investigate collateral effects that arise during daptomycin adaptation of E. faecalis in four independent laboratory-evolved populations. Using a combination of growth assays and both single isolate and population sequencing, we identified DAP-resistant lineages with mutations in one or more genes previously associated with DAP resistance, and these isolates are characterized by divergent phenotypic properties-including different levels of DAP resistance and different growth rates (i.e. fitness costs) in drug-free media. Interestingly, we also observed strongly divergent collateral responses to different antibiotics, particularly CRO, with collateral resistance arising in mutants harboring DAP-resistance mutations in cardiolipin synthetase (cls) or in genes linked to the two-component signaling system YxdJK (bceR or a regulated transporter ycvR). By contrast, mutations in liaX, a component of a LiaFSR two-component signaling system, arose in two of the four populations, with point mutations associated with CRO-sensitivity and a large structural integration of plasmid pTEF3 associated with extreme CRO-sensitivity and a dramatically reduced growth rate. Our results reveal considerable phenotypic differences in mutations targeting the LiaSFR system and highlight trade-offs between resistance to daptomycin, collateral profiles (most notably to CRO), and drug-free growth rates in evolving lineages. As a whole, these results underscore how rich-and remarkably diverse-evolutionary dynamics can emerge even in parallel populations adapting to simple daptomycin escalation protocols.

evolutionary biology↗

bioRxiv2023

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria, based on a rescaling approach (Gjini and Wood, 2021). We show how the resistance to drugs in space, and the consequent adaptation of growth rate is governed by a Price equation with diffusion. The covariance terms in this equation integrate features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits, to the relative advantage of each mutant across the environment. Such a mathematical understanding allows to predict the precise outcomes of selection over space, ultimately from the fundamental balance between growth and movement traits, and their diversity in a population.

evolutionary biology↗

bioRxiv2022

Scale-free correlations and criticality in explants derived from an orthotopic model of brain cancer

Collective behavior spans several orders of magnitudes of biological organization, ranging from cell colonies, to flocks of birds, to herds of wildebeests. In this work, we investigate collective motion of glioblastoma cells in an ex-vivo experimental model of malignant brain tumors. Using time-resolved tracking of individual glioma cells, we observed collective motion characterized by weak polarization in the (directional) velocities of single cells, with fluctuations correlated over many cell lengths. The correlation length of these fluctuations scales approximately linearly with the total population size, and these scale-free correlations suggest that the system is poised near a critical point. To further investigate the source of this scale-free behavior, we used a data-driven maximum entropy model to estimate the effective length scale (nc) and strength (J) of local interactions between tumor cells. The model captures statistical features of the experimental data, including the shape of the velocity distributions and the existence of long range correlations, and suggests that nc and J vary substantially across different populations. However, the scale and strength of the interactions do not vary randomly, but instead occur on the boundary separating ordered and disordered motion, where the model exhibits classical signs of criticality, including divergences in generalized susceptibility and heat capacity. Our results suggest that brain tumor assemblies are poised near a critical point characterized by scale-free correlations in the absence of strong polarization.

biophysics↗

bioRxiv2022

Drug dependence in cancer is exploitable by optimally constructed treatment holidays

Recent work in cell culture models, animal models, and human patients indicates that cancers with acquired resistance to a drug can become simultaneously dependent upon the presence of that drug for survival. This drug dependence offers a potential avenue for improving treatments aimed at slowing resistance, yet relatively little is known about the frequency with which drug dependence arises, the mechanisms underlying that dependence, and how drug schedules might be tuned to optimally exploit drug dependence. In this work, we address these open questions using a combination of laboratory evolution, in vitro experiments, and simple mathematical models. First, we used laboratory evolution to select more than 100 resistant BRAF mutant melanoma cell lines with acquired resistance to BRAF, MEK, or ERK inhibitors. We found that nearly half of these lines exhibit drug dependence, and the dependency response is associated with EGFR-driven senescence induction, but not apoptosis, following drug withdrawal. Then, using melanoma populations with evolved resistance to the BRAF inhibitor PLX4720, we showed that drug dependence can be leveraged to dramatically reduce population growth when treatment strategies include optimally chosen drug-free "holidays". On short timescales, the duration of these holidays depends sensitively on the composition of the population, but for sufficiently long treatments it depends only on a single dimensionless parameter ({gamma}) that describes how the growth rates of each cell type depend on the different treatment environments. Experiments confirm that the optimal holiday duration changes in time-with holidays of different durations leading to optimized treatments on different timescales. Furthermore, we find that the presence of "non-dependent" resistant cells does not change the optimal treatment schedule but leads to a net increase in population size. Finally, we show that even in the absence of detailed information about the composition and growth characteristics of cellular clones within a population, a simple adaptive therapy protocol can produce near-optimal outcomes using only measurements of total population size, at least when these measurements are sufficiently frequent. As a whole, these results may provide a stepping-stone toward the eventual development of evolution-inspired treatment strategies for drug dependent cancers.

cancer biology↗

bioRxiv2021

Dynamic collateral sensitivity profiles highlight challenges and opportunities for optimizing antibiotic sequences

As failure rates for traditional antimicrobial therapies escalate, recent focus has shifted to evolution-based therapies to slow resistance. Collateral sensitivity-the increased susceptibility to one drug associated with evolved resistance to a different drug-offers a potentially exploitable evolutionary constraint, but the manner in which collateral effects emerge over time is not well understood. Here, we use laboratory evolution in the opportunistic pathogen E. faecalis to phenotypically characterize collateral profiles through evolutionary time. Specifically, we measure collateral profiles for 400 strain-antibiotic combinations over the course of 4 evolutionary time points as strains are selected in increasing concentrations of antibiotic. We find that at a global level-when results from all drugs are combined-collateral resistance dominates during early phases of adaptation, when resistance to the selecting drug is lower, while collateral sensitivity becomes increasingly likely with further selection. At the level of individual populations, however, the trends are idiosyncratic; for example, the frequency of collateral sensitivity to ceftriaxone increases over time in isolates selected by linezolid but decreases is isolates selected by ciprofloxacin. Finally, we show experimentally how dynamic collateral sensitivity relationships can lead to time-dependent dosing windows that depend on finely timed switching between drugs.

evolutionary biology↗

bioRxiv2020

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction, DI) and how resistance to one drug impacts resistance to the other (collateral effects, CE). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug sensitive (ancestral) and drug resistant (mutant) populations. We show that evolved resistance to the component drugs-and in turn, the adaptation of growth rate-is governed by a Price equation whose covariance terms encode geometric features of both the two-drug response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. Our results clarify the complex relationship between drug interactions and collateral effects in multi-drug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally-explicit selective outcomes.

evolutionary biology↗

bioRxiv2020

Spatial segregation and cooperation in radially expanding microbial colonies under antibiotic stress

Antibiotic resistance in microbial communities reflects a combination of processes operating at different scales. The molecular mechanisms underlying antibiotic resistance are increasingly understood, but less is known about how these molecular events give rise to spatiotemporal behavior on longer length scales. In this work, we investigate the population dynamics of bacterial colonies comprised of drug-resistant and drug-sensitive cells undergoing range expansion under antibiotic stress. Using the opportunistic pathogen E. faecalis with plasmid-encoded ({beta}-lactamase) resistance as a model system, we track colony expansion dynamics and visualize spatial pattern formation in fluorescently labeled populations exposed to ampicillin, a commonly-used {beta}-lactam antibiotic. We find that the radial expansion rate of mixed communities is approximately constant over a wide range of drug concentrations and initial population compositions. Fluorescence imaging of the final populations shows that resistance to ampicillin is cooperative, with sensitive cells surviving in the presence of resistant cells even at drug concentrations lethal to sensitive-only communities. Furthermore, despite the relative invariance of expansion rate across conditions, the populations exhibit a diverse range of spatial segregation patterns, with both the spatial structure and the population composition depending on drug concentration, initial composition, and initial population size. Agent based models indicate that the observed dynamics are consistent with long-range cooperation, despite the fact that {beta}-lactamase remains cell-associated in E. faecalis, and experiments confirm that resistant colonies provide a protective effect to sensitive cells on length scales multiple times the size of a single colony. Furthermore, in the limit of small inoculum sizes, we experimentally show that populations seeded with (on average) no more than a single resistant cell can produce mixed communities in the presence of drug. While biophysical models of diffusion-limited drug degradation suggest that individual resistant cells offer only short-range protection to neighboring sensitive cells, we show that long-range protection may arise from synergistic effects of multiple resistant cells, even when they represent only a small fraction of a colonys surface area. Our results suggest that {beta}-lactam resistance can be cooperative even in spatially extended systems where genetic segregation typically disfavors exploitation of locally produced public goods.

microbiology↗

bioRxiv2019

Evolution in alternating environments with tunable inter-landscape correlations

Natural populations are often exposed to temporally varying environments. Evolutionary dynamics in varying environments have been extensively studied, though understanding the effects of varying selection pressures remains challenging. Here we investigate how cycling between a pair of statistically related fitness landscapes affects the evolved fitness of an asexually reproducing population. We construct pairs of fitness landscapes that share global fitness features but are correlated with one another in a tunable way, resulting in landscape pairs with specific correlations. We find that switching between these landscape pairs, depending on the ruggedness of the landscape and the inter-landscape correlation, can either increase or decrease steady-state fitness relative to evolution in single environments. In addition, we show that switching between rugged landscapes often selects for increased fitness in both landscapes, even in situations where the landscapes themselves are anti-correlated. We demonstrate that positively correlated landscapes often possess a shared maximum in both landscapes that allows the population to step through sub-optimal local fitness maxima that often trap single landscape evolution trajectories. Finally, we demonstrate that switching between anti-correlated paired landscapes leads to ergodic-like dynamics where each genotype is populated with nonzero probability, dramatically lowering the steady-state fitness in comparison to single landscape evolution.

evolutionary biology↗

bioRxiv2019

Fluorescent reporter plasmids for single-cell and bulk-level composition assays in E. faecalis

Fluorescent reporters are an important tool for monitoring dynamics of bacterial populations at the single cell and community level. While there are a large range of reporter constructs available-particularly for common model organisms like E. coli-fewer options exist for other species, including E. faecalis, a gram-positive opportunistic pathogen. To expand the potential toolkit available for E. faecalis, we modified a previously developed reporter plasmid (pBSU101) to express one of nine different fluorescent reporters and confirmed that all constructs exhibited detectable fluorescence in single E. faecalis cells and mixed biofilm communities. To identify promising constructs for bulk-level experiments, we then measured the fluorescence spectra from E. faecalis populations in microwell plate (liquid) cultures during different growth phases. Cultures showed density- and reporter-specific variations in fluorescent signal, though spectral signatures of all reporters become clear in late-exponential and stationary-phase populations. Based on these results, we identified six pairs of reporters that can be combined with simple spectral unmixing to accurately estimate population composition in 2-strain mixtures at or near stationary phase. This approach offers a simple and scalable method for selection and competition experiments in simple two-species populations. Finally, we modified the construct to express codon-optimized variants of blue (BFP) and red (RFP) reporters and show that they lead to increased fluorescence in exponentially growing cells. As a whole, the results inform the scope of application of different reporters and identify both single reporters and reporter pairs that are promising for fluorescence-based assays at bulk and single-cell levels in E. faecalis.

microbiology↗

bioRxiv2019

Delayed antibiotic exposure induces population collapse in enterococcal communities with drug-resistant subpopulations

Bacteria exploit a diverse set of defenses to survive exposure to antibiotics. While the molecular and genetic underpinnings of antibiotic resistance are increasingly understood, less is known about how these molecular events influence microbial dynamics on the population scale. In this work, we show that the dynamics of E. faecalis communities exposed to antibiotics can be surprisingly rich, revealing scenarios where-for example-increasing population size or delaying drug exposure can promote population collapse. Specifically, we combine experiments in computer-controlled bioreactors with simple mathematical models to reveal density-dependent feedback loops that couple population growth and antibiotic efficacy when communities include drug-resistant ({beta}-lactamase producing) subpopulations. The resulting communities exhibit a wide range of behavior, including population survival, population collapse, or one of two qualitatively distinct bistable behaviors where survival is favored in either small or large populations. These dynamics reflect competing density-dependent effects of different subpopulations, with growth of drug-sensitive cells increasing but growth of drug-resistant cells decreasing effective drug inhibition. Guided by these results, we experimentally demonstrate how populations receiving immediate drug influx may sometimes thrive, while identical populations exposed to delayed drug influx (and lower average drug concentrations) collapse. These results illustrate that the spread of drug resistant determinants--even in a simplified single-species communities--may be governed by potentially counterintuitive dynamics driven by population-level interactions.

microbiology↗

bioRxiv2019

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

Antibiotic combinations are increasingly used to combat bacterial infections. Multidrug therapies are a particularly important treatment option for E. faecalis, an opportunistic pathogen that contributes to high-inoculum infections such as infective endocarditis. While numerous synergistic drug combinations for E. faecalis have been identified, much less is known about how different combinations impact the rate of resistance evolution. In this work, we use high-throughput laboratory evolution experiments to quantify adaptation in growth rate and drug resistance of E. faecalis exposed to drug combinations exhibiting different classes of interactions, ranging from synergistic to suppressive. We identify a wide range of evolutionary behavior, including both increased and decreased rates of growth adaptation, depending on the specific interplay between drug interaction and cross resistance. For example, selection in a dual-lactam combination leads to accelerated growth adaptation compared to selection with the individual drugs, even though the resulting resistance profiles are nearly identical. On the other hand, populations evolved in an aminoglycoside and -lactam combination exhibit decreased growth adaptation and resistant profiles that depend on the specific drug concentrations. We show that the main qualitative features of these evolutionary trajectories can be explained by simple rescaling arguments that correspond to geometric transformations of the two-drug growth response surfaces measured in ancestral cells. The analysis also reveals multiple examples where resistance profiles selected by drug combinations correspond to (nearly) optimized linear combinations of those selected by the component drugs. Our results highlight trade-offs between drug interactions and collateral effects during the evolution of multi-drug resistance and emphasize evolutionary benefits and disadvantages of particular drug pairs targeting enterococci.

evolutionary biology↗

bioRxiv2019

Sensitive cells extend antibiotic-driven containment of drug-resistant bacterial populations

Standard infectious disease practice calls for aggressive drug treatment that rapidly eliminates the pathogen population before resistance can emerge. When resistance is absent, this elimination strategy can lead to complete cure. However, it also removes drug-sensitive cells as quickly as possible, removing competitive barriers that may slow the growth of resistant cells. In contrast to the elimination strategy, the containment strategy aims to maintain the maximum tolerable number of pathogens, exploiting competitive suppression to achieve chronic control. Here we combine in vitro experiments in computer-controlled bioreactors with mathematical modeling to investigate whether containment strategies can delay failure of antibiotic treatment regimens. To do so, we measured the \"escape time\" required for drug-resistant E. coli populations to eclipse a threshold density maintained by adaptive antibiotic dosing. Populations containing only resistant cells rapidly escape containment, but we found that matched populations with the maximum possible number of sensitive cells could be contained for significantly longer. The increase in escape time occurs only when the threshold density-the acceptable bacterial burden-is sufficiently high, an effect that mathematical models attribute to increased competition. The findings provide decisive experimental confirmation that maintaining the maximum number of sensitive cells can be used to contain resistance when the size of the population is sufficiently large.

microbiology↗

bioRxiv2019

Using selection by non-antibiotic stressors to sensitize bacteria to antibiotics

O_SCPCAPABSTRACTC_SCPCAPBacterial resistance to one antibiotic is frequently accompanied by crossresistance to other drugs. Similarly, non-antibiotic selective forces, from biocides to osmotic stress, have been shown to decrease antibiotic susceptibility, often the result of shared, non-specific resistance mechanisms. On the other hand, evolved resistance to particular antibiotics may also be associated with increased sensitivity to other drugs, highlighting evolutionary constraints that could form the basis for novel anti-resistance strategies. While recent studies indicate this collateral sensitivity is common between antibiotics, much less is known about potentially sensitizing effects of non-antibiotic stressors. In this study, we use laboratory evolution to investigate adaptation of E. faecalis, an opportunistic bacterial pathogen, to a broad collection of environmental agents, ranging from antibiotics and biocides to extreme pH and osmotic stress. We find that non-antibiotic selection frequently leads to increased sensitivity to other conditions, including multiple antibiotics. Using population sequencing and whole genome sequencing of single isolates from the evolved populations, we identify multiple mutations in genes previously linked with resistance to the selecting conditions, including genes corresponding to known drug targets or multi-drug efflux systems previously tied to collateral sensitivity. Finally, we hypothesized based on the measured sensitivity profiles that sequential rounds of antibiotic and non-antibiotic selection may lead to hypersensitive populations by harnessing the orthogonal collateral effects of particular pairs of selective forces. To test this hypothesis, we show experimentally that populations evolved to a sequence of linezolid (an oxazolidinone antibiotic) and sodium benzoate (a common preservative) exhibit increased sensitivity to more stressors than adaptation to either condition alone. The results demonstrate how sequential adaptation to drug and non-drug environments can be used to sensitize bacterial to antibiotics and highlight new potential strategies for exploiting shared constraints governing adaptation to diverse environmental challenges.

microbiology↗